All of the models presented here are considered “static” in the sense that they model a single categorical latent variable where an individual’s class membership does not change. These models include latent class analysis (LCA), latent profile analysis (LPA), and mixed indicator models. Although different names for these models appear throughout the literature, here we use the convention that models that include only categorical indicators are LCAs, include only continuous indicators are LPAs, and include both categorical and continuous indicators are mixed indicator latent class models.

LCA: Baseline LCA with 3+ level categorical indicators

This code fits a longitudinal latent class model, using categorical indicators with 3+ levels, to identify latent classes indicated by multidimensional experiences of racism and heterosexism during the transition to adulthood among sexual minority men of color.

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LCA: Latent class moderation

This code demonstrates how to use a latent class moderator to examine heterogeneity in intervention effects among adolescents receiving treatment for cannabis use. First, the code identifies latent classes of contextual and individual risk at baseline using LCA. Then, it uses an adjusted 3-step approach with BCH weights to regress the outcomes on level of care, latent class membership, the interaction between them, and covariates.

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LCA: LCA with a grouping variable and without measurement variance

This code fits a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. It includes a grouping variable for year, and observations came from 3 different years. Measurement invariance across groups is not imposed resulting in an unrestricted latent class model with multiple groups.

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